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This course introduces to the analyses of online communities and social networks. Students will learn to formulate scientific questions about the online dynamics and use open software to collect, organize, model, and communicate data from common social networks.
We are social animals, we live in communities, we interact and shape the world together. More and more, these activities are performed online. This course introduces the mathematical, statistical and data science tools necessary to analyse scientifically the behaviour of people on Social Networks. The students will learn topics from linear algebra (matrix algebra, some spectral theory, ...), graph theory (distances, paths, ...), complex network theory (centralities, power-law distributions, homophily), statistics (exponential random models, random dot product graph models, ...), and more. The course puts a special emphasis on interdisciplinary investigation approaches. Current algorithmic implementations and robust computational frameworks will be used in the weekly laboratories.The course syllabus is flexible, and adapts to the students' interest picking from the wide palette of tools used in social network analysis.Guests from industry and/or academia may contribute their expertise.Assessments are designed to encourage the student to build a data science portfolio of projects and public writing.
During this course, students will learn to: use statistical and mathematical notions to understand and explore complex networks;formulate scientific questions about the dynamics of online social networks;use open source software to collect, organize, model, and communicate data from common social networks.
Subject to approval of the Head of Department of Mathematics and Statistics.
Giulio Dalla Riva
Essay 30%Tutorial 30%Group Project 30%
Domestic fee $969.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics